Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models

Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of...

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Vydané v:	ISPRS journal of photogrammetry and remote sensing Ročník 66; číslo 6; s. 894 - 906
Hlavní autori:	Darvishzadeh, Roshanak, Atzberger, Clement, Skidmore, Andrew, Schlerf, Martin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.11.2011 Elsevier
Predmet:	Accuracy ancillary information Animal, plant and microbial ecology Applied geophysics band vegetation indexes Biological and medical sciences canopy biophysical variables crop chlorophyll content Earth sciences Earth, ocean, space Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques Grasslands heterogeneous grassland Hyperspectral hyperspectral imagery Internal geophysics Inversions Italy Leaf area index least squares Mapping Mapping LAI Mediterranean grassland Modeling national parks optical-properties Partial least square regression physical models precision agriculture Radiative transfer red edge position reflectance data remote-sensing data Teledetection and vegetation maps Vegetation Vegetation indices Italy Narrows New York Southern Europe United States Europe Mapping LAI Modeling Mediterranean grassland Hyperspectral Vegetation indices Partial least square regression inverse problem Mediterranean vegetation radiative transfer accuracy least-squares Offshore discharge Image North America in situ grasslands physical models regression imagery performances Leaf area index national parks
ISSN:	0924-2716, 1872-8235
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Abstract	Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R 2 between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches ( R 2 = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data ( R 2 = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements.
AbstractList	Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R2 between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches (R2 = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data (R2 = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements. Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R 2 between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches ( R 2 = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data ( R 2 = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements. Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R² between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches (R²=0.89, nRMSE=0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data (R²=0.91, nRMSE=0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements. Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R super(2) between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches (R super(2) = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data (R super(2) = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements.
Author	Schlerf, Martin Darvishzadeh, Roshanak Skidmore, Andrew Atzberger, Clement
Author_xml	– sequence: 1 givenname: Roshanak surname: Darvishzadeh fullname: Darvishzadeh, Roshanak email: darvish@itc.nl, r_darvish@sbu.ac.ir organization: Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran – sequence: 2 givenname: Clement surname: Atzberger fullname: Atzberger, Clement organization: University of Natural Resources and Life Sciences, Vienna, Institute of Surveying, Remote Sensing and Land Information, Peter Jordan-Strasse 82, 1190 Vienna, Austria – sequence: 3 givenname: Andrew surname: Skidmore fullname: Skidmore, Andrew organization: University of Twente, faculty of Geo-information Science and Earth Observation (ITC), Hengelosestraat 99, 7514 AE Enschede, The Netherlands – sequence: 4 givenname: Martin surname: Schlerf fullname: Schlerf, Martin organization: University of Twente, faculty of Geo-information Science and Earth Observation (ITC), Hengelosestraat 99, 7514 AE Enschede, The Netherlands
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Copyright	2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) 2015 INIST-CNRS Wageningen University & Research
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Keywords	Mapping LAI Modeling Mediterranean grassland Hyperspectral Vegetation indices Partial least square regression inverse problem Mediterranean vegetation radiative transfer accuracy least-squares Offshore discharge Image North America in situ grasslands physical models regression imagery performances Leaf area index national parks
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Snippet	Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf...
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SubjectTerms	Accuracy ancillary information Animal, plant and microbial ecology Applied geophysics band vegetation indexes Biological and medical sciences canopy biophysical variables crop chlorophyll content Earth sciences Earth, ocean, space Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques Grasslands heterogeneous grassland Hyperspectral hyperspectral imagery Internal geophysics Inversions Italy Leaf area index least squares Mapping Mapping LAI Mediterranean grassland Modeling national parks optical-properties Partial least square regression physical models precision agriculture Radiative transfer red edge position reflectance data remote-sensing data Teledetection and vegetation maps Vegetation Vegetation indices
Title	Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models
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